Tracking a set of mobile objects with one or more sensors consists in exploiting over time the data output by the group of at least one sensor to construct and update a set of tracks corresponding to the various objects perceived by the one or more sensors. It is a question of grouping as time passes the measurements made for the various tracked objects so as to obtain homogeneous groupings, each grouping having to correspond to a different object, and each grouping constituting a track following the same object as time passes. The objects to be tracked and the one or more sensors may be moving.
The data corresponding to the various mobile objects present in the environment monitored by the one or more sensors may for example include information on the position of said objects. Depending on the sensor, the data may be measured values, such as for example an azimuth in the case of an ESM (electronic support measure) sensor, or measurement vectors such as for example vectors consisting of an azimuth and a distance in the case of a radar sensor, or vectors consisting of an azimuth and an elevation in the case of an optronic sensor. The data vectors may also contain characteristic measurements describing the objects. In certain cases, the position information may consist of a dated position of the sensor and one or more parameters relating to the location of the object in space relative to the sensor.
One problem, in the field of the tracking of mobile objects, is that of correctly tracking the progress of the various objects over time. When similar objects, i.e. objects that are indiscriminable by the characteristic measurements taken by one or more sensors, move through space it is possible that they cross over in terms of their perception from the carrier of the one or more sensors, i.e. that the measurements of the relative position of the objects with respect to said carrier momentarily coincide. Ambiguities are then spoken of. In this case, it becomes difficult, over a more or less long duration, to know whether the information produced by the one or more sensors corresponds to one object or to another. One problem that arises is that of knowing how to group and track the measurements when some of them become momentarily ambiguous. Under these conditions, during the processing of the data, mixups may occur and one or more measurements may be attributed to incorrect tracks. This may degrade the quality of the tracking of the objects and may lead to incorrect characterizations, discontinuities or track splitting at the moment of the crossover. In this context, it is advantageous to have a solution allowing the processing of the tracking to be improved during these crossover situations.
The tracking of objects or of targets is widely treated in the literature (see for example Yaakov Bar-Shalom, Xiao-Rong Li—“Estimation and Tracking”—Artech House 1993 or Samuel Blackman, Robert Popoli—“Design and Analysis of Modern Tracking Systems”—Artech House 1999). This tracking consists in associating newly produced information with tracks already produced in the past while taking into account the proximity of the measured values and of measurement noise, and in updating the tracks.
The tracking of multiple targets (multiple target tracking or MTT) by a sensor involves processing that consists in creating or updating tracks on the basis of newly acquired data. This is typically done (see in particular Samuel Blackman, Robert Popoli—“Design and Analysis of Modern Tracking Systems”—Artech House 1999) by chaining five functions: processing of new observations, association of the observations with tracks, management of the tracks (initialization, confirmation or deletion), filtering and prediction (to update the tracks and to enable estimation of positions in the near future), and windowing (in order to allow associations in the near future while restricting the possible associations to a volume about the predicted position).
However sometimes a number of tracks may conflict, for example when there is an intersection of the volumes about predicted positions. In nearest neighbor (NN) processing, each track is updated by the closest observation, even if the observation is compatible with a plurality of tracks. In global nearest neighbor (GNN) processing, the association is made while considering all the associations compatible with the windowing, but under the constraint that an observation can be associated only with at most one track.
The kinematic parameters of the tracks are typically updated by Kalman filtering (see Yaakov Bar-Shalom, Xiao-Rong Li—“Estimation and Tracking”—Artech House 1993) or by interacting multiple model (IMM) processing (see Yaakov Bar-Shalom, Xiao-Rong Li—“Multitarget-multisensor tracking: Principles and techniques”—1995 or Samuel Blackman, Robert Popoli—“Design and Analysis of Modern Tracking Systems”—Artech House 1999) if it is desired to use an array of Kalman filters in parallel to make it possible to adapt to a kinematic change. However, S. Blackman notes that GNN association processing associated with Kalman filtering only functions well when the targets are widely spaced (see in particular Samuel Blackman—“Multiple Hypothesis Tracking For Multiple Target Tracking”—IEEE A&E Systems Magazine, January 2004 vol. 19, no. 1, Part 2: tutorials, p5-18). In situations of conflict between tracks, the covariance matrix of the Kalman filter may be increased, but this may further increase the conflicts.
It is also known in the prior art to use a joint probabilistic data association (JPDA) approach, as for example described in 1995 by Yaakov Bar-Shalom et Xiao-Rong Li in “Multitarget-multisensor tracking: Principles and techniques”. This method consists in updating the tracks with all the observations compatible with the windowing using a sum of the observations weighted by their probability. The drawback of this method is that it tends to make tracks that are closely spaced agglomerate.
Another way of managing cases of conflicts during associations is to use multiple hypothesis tracking (MHT) techniques such as for example introduced by D. B. Reid in “An algorithm for tracking multiple targets”—IEEE Transactions on Automatic Control, vol. 21, no. 1 (February 1976), p 101-104. This type of processing consists, in cases of association ambiguity, in memorizing and maintaining over time all the combinations of possible successions of associations between observations and tracks. In order to attempt to preserve only those tracks that persist over time, a score may be defined for the hypotheses, and only the best hypotheses finally adopted. These hypothesis scores may be defined by a likelihood ratio or by a log likelihood ratio of the likelihood that it be a track to the likelihood that it be a false alarm. This type of processing generates a combinatorial explosion that may be controlled by clustering, rejection or grouping of hypotheses, or by retaining only the k best hypotheses. It will be noted that these approaches use a priori probability hypotheses on the number of objects or false-alarm rates.